OpenRaiser/Intern-Atlas huggingface dataset:
Noncooperative Games for Autonomous Consumer Load Balancing over Smart Grid
Venue: arXiv cs.GT 2011
Year: 2011

Traditionally, most consumers of electricity pay for their consumptions according to a fixed rate. With the advancement of Smart Grid technologies, large-scale implementation of variable-rate metering becomes more practical. As a result, consumers will be able to control their electricity consumption in an automated fashion, where one possible scheme is to have each individual maximize its own utility as a noncooperative game. In this paper, noncooperative games are formulated among the electricity consumers in Smart Grid with two real-time pricing schemes, where the Nash equilibrium operation points are investigated for their uniqueness and load balancing properties. The first pricing scheme charges a price according to the average cost of electricity borne by the retailer and the second one charges according to a time-variant increasing-block price, where for each scheme, a zero-revenue model and a constant-rate revenue model are considered. In addition, the relationship between the studied games and certain competitive routing games from the computer networking community, known as atomic flow games, is established, for which it is shown that the proposed noncooperative game formulation falls under the class of atomic splittable flow games. The Nash equilibrium is shown to exist for four different combined cases corresponding to the two pricing schemes and the two revenue models, and is unique for three of the cases under certain conditions. It is further shown that both pricing schemes lead to similar electricity loading patterns when consumers are only interested in minimizing the electricity costs without any other profit considerations. Finally, the conditions under which the increasing-block pricing scheme is preferred over the average-cost based pricing scheme are discussed.
Fixed Block Compression Boosting in FM-Indexes
Venue: arXiv cs.DS 2011
Year: 2011

A compressed full-text self-index occupies space close to that of the compressed text and simultaneously allows fast pattern matching and random access to the underlying text. Among the best compressed self-indexes, in theory and in practice, are several members of the FM-index family. In this paper, we describe new FM-index variants that combine nice theoretical properties, simple implementation and improved practical performance. Our main result is a new technique called fixed block compression boosting, which is a simpler and faster alternative to optimal compression boosting and implicit compression boosting used in previous FM-indexes.
Collective Construction of 2D Block Structures with Holes
Venue: arXiv cs.CG 2011
Year: 2011

In this paper we present algorithms for collective construction systems in which a large number of autonomous mobile robots trans- port modular building elements to construct a desired structure. We focus on building block structures subject to some physical constraints that restrict the order in which the blocks may be attached to the structure. Specifically, we determine a partial ordering on the blocks such that if they are attached in accordance with this ordering, then (i) the structure is a single, connected piece at all intermediate stages of construction, and (ii) no block is attached between two other previously attached blocks, since such a space is too narrow for a robot to maneuver a block into it. Previous work has consider this problem for building 2D structures without holes. Here we extend this work to 2D structures with holes. We accomplish this by modeling the problem as a graph orientation problem and describe an O(n^2) algorithm for solving it. We also describe how this partial ordering may be used in a distributed fashion by the robots to coordinate their actions during the building process.
General Linearized Polynomial Interpolation and Its Applications
Venue: arXiv cs.NI 2011
Year: 2011

In this paper, we first propose a general interpolation algorithm in a free module of a linearized polynomial ring, and then apply this algorithm to decode several important families of codes, Gabidulin codes, KK codes and MV codes. Our decoding algorithm for Gabidulin codes is different from the polynomial reconstruction algorithm by Loidreau. When applied to decode KK codes, our interpolation algorithm is equivalent to the Sudan-style list-1 decoding algorithm proposed by K/"otter and Kschischang for KK codes. The general interpolation approach is also capable of solving the interpolation problem for the list decoding of MV codes proposed by Mahdavifar and Vardy, and has a lower complexity than solving linear equations.
Auto-Stabilisation et Confinement de Fautes Malicieuses : Optimalité du Protocole min+1
Venue: arXiv cs.DC 2011
Year: 2011

A self-stabilizing is naturally resilient to transients faults (that is, faults of finite duration). Recently, a new class of protocol appears. These protocols are self-stabilizing and are moreover resilient to a limited number of permanent faults. In this article, we interest in self-stabilizing protocols that tolerate very hard permanent faults: Byzantine faults. We introduce two new scheme of Byzantine containment in self-stabilizing systems. We show that, for the problem of BFS spanning tree construction, the well known self-stabilizing protocol min+1 provides without significant modification the best Byzantine containment with respect to these new schemes.
Wireless MIMO Switching
Venue: arXiv cs.IT 2011
Year: 2011

In a generic switching problem, a switching pattern consists of a one-to-one mapping from a set of inputs to a set of outputs (i.e., a permutation). We propose and investigate a wireless switching framework in which a multi-antenna relay is responsible for switching traffic among a set of $N$ stations. We refer to such a relay as a MIMO switch. With beamforming and linear detection, the MIMO switch controls which stations are connected to which stations. Each beamforming matrix realizes a permutation pattern among the stations. We refer to the corresponding permutation matrix as a switch matrix. By scheduling a set of different switch matrices, full connectivity among the stations can be established. In this paper, we focus on "fair switching" in which equal amounts of traffic are to be delivered for all $N(N-1)$ ordered pairs of stations. In particular, we investigate how the system throughput can be maximized. In general, for large $N$ the number of possible switch matrices (i.e., permutations) is huge, making the scheduling problem combinatorially challenging. We show that for N=4 and 5, only a subset of $N-1$ switch matrices need to be considered in the scheduling problem to achieve good throughput. We conjecture that this will be the case for large $N$ as well. This conjecture, if valid, implies that for practical purposes, fair-switching scheduling is not an intractable problem.
Data Mining : A prediction of performer or underperformer using classification
Venue: arXiv cs.DB 2011
Year: 2011

Now a day's students have a large set of data having precious information hidden. Data mining technique can help to find this hidden information. In this paper, data mining techniques name Byes classification method is used on these data to help an institution. Institutions can find those students who are consistently perform well. This study will help to institution reduce the drop put ratio to a significant level and improve the performance level of the institution.
Distributed Self-Organization Of Swarms To Find Globally $ε$-Optimal Routes To Locally Sensed Targets
Venue: arXiv cs.RO 2011
Year: 2011

The problem of near-optimal distributed path planning to locally sensed targets is investigated in the context of large swarms. The proposed algorithm uses only information that can be locally queried, and rigorous theoretical results on convergence, robustness, scalability are established, and effect of system parameters such as the agent-level communication radius and agent velocities on global performance is analyzed. The fundamental philosophy of the proposed approach is to percolate local information across the swarm, enabling agents to indirectly access the global context. A gradient emerges, reflecting the performance of agents, computed in a distributed manner via local information exchange between neighboring agents. It is shown that to follow near-optimal routes to a target which can be only sensed locally, and whose location is not known a priori, the agents need to simply move towards its "best" neighbor, where the notion of "best" is obtained by computing the state-specific language measure of an underlying probabilistic finite state automata. The theoretical results are validated in high-fidelity simulation experiments, with excess of $10^4$ agents.
Random input helps searching predecessors
Venue: arXiv cs.DS 2011
Year: 2011

We solve the dynamic Predecessor Problem with high probability (whp) in constant time, using only $n^{1+δ}$ bits of memory, for any constant $δ> 0$. The input keys are random wrt a wider class of the well studied and practically important class of $(f_1, f_2)$-smooth distributions introduced in \cite{and:mat}. It achieves O(1) whp amortized time. Its worst-case time is $O(\sqrt{\frac{\log n}{\log \log n}})$. Also, we prove whp $O(\log \log \log n)$ time using only $n^{1+ \frac{1}{\log \log n}}= n^{1+o(1)}$ bits. Finally, we show whp $O(\log \log n)$ time using O(n) space.
Super-d-complexity of finite words
Venue: arXiv cs.DM 2011
Year: 2011

In this paper we introduce and study a new complexity measure for finite words. For positive integer $d$ special scattered subwords, called super-$d$-subwords, in which the gaps are of length at least $(d-1)$, are defined. We give methods to compute super-$d$-complexity (the total number of different super-$d$-subwords) in the case of rainbow words (with pairwise different letters) by recursive algorithms, by mahematical formulas and by graph algorithms. In the case of general words, with letters from a given alphabet without any restriction, the problem of the maximum value of the super-$d$-complexity of all words of length $n$ is presented.
Arc-preserving subsequences of arc-annotated sequences
Venue: arXiv cs.CC 2011
Year: 2011

Arc-annotated sequences are useful in representing the structural information of RNA and protein sequences. The longest arc-preserving common subsequence problem has been introduced as a framework for studying the similarity of arc-annotated sequences. In this paper, we consider arc-annotated sequences with various arc structures. We consider the longest arc preserving common subsequence problem. In particular, we show that the decision version of the 1-{\sc fragment LAPCS(crossing,chain)} and the decision version of the 0-{\sc diagonal LAPCS(crossing,chain)} are {\bf NP}-complete for some fixed alphabet $Σ$ such that $|Σ| = 2$. Also we show that if $|Σ| = 1$, then the decision version of the 1-{\sc fragment LAPCS(unlimited, plain)} and the decision version of the 0-{\sc diagonal LAPCS(unlimited, plain)} are {\bf NP}-complete.
Towards a Data Reduction for the Minimum Flip Supertree Problem
Venue: arXiv cs.DS 2011
Year: 2011

In computational phylogenetics, the problem of constructing a supertree of a given set of rooted input trees can be formalized in different ways, to cope with contradictory information in the input. We consider the Minimum Flip Supertree problem, where the input trees are transformed into a 0/1/?-matrix, such that each row represents a taxon, and each column represents an inner node of one of the input trees. Our goal is to find a perfect phylogeny for the input matrix requiring a minimum number of 0/1-flips, that is, corrections of 0/1-entries in the matrix. The problem is known to be NP-complete. Here, we present a parameterized data reduction with polynomial running time. The data reduction guarantees that the reduced instance has a solution if and only if the original instance has a solution. We then make our data reduction parameter-independent by using upper bounds. This allows us to preprocess an instance, and to solve the reduced instance with an arbitrary method. Different from an existing data reduction for the consensus tree problem, our reduction allows us to draw conclusions about certain entries in the matrix. We have implemented and evaluated our data reduction. Unfortunately, we find that the Minimum Flip Supertree problem is also hard in practice: The amount of information that can be derived during data reduction diminishes as instances get more "complicated", and running times for "complicated" instances quickly become prohibitive. Still, our method offers another route of attack for this relevant phylogenetic problem.
Parallel Breadth-First Search on Distributed Memory Systems
Venue: arXiv cs.DC 2011
Year: 2011

Data-intensive, graph-based computations are pervasive in several scientific applications, and are known to to be quite challenging to implement on distributed memory systems. In this work, we explore the design space of parallel algorithms for Breadth-First Search (BFS), a key subroutine in several graph algorithms. We present two highly-tuned parallel approaches for BFS on large parallel systems: a level-synchronous strategy that relies on a simple vertex-based partitioning of the graph, and a two-dimensional sparse matrix-partitioning-based approach that mitigates parallel communication overhead. For both approaches, we also present hybrid versions with intra-node multithreading. Our novel hybrid two-dimensional algorithm reduces communication times by up to a factor of 3.5, relative to a common vertex based approach. Our experimental study identifies execution regimes in which these approaches will be competitive, and we demonstrate extremely high performance on leading distributed-memory parallel systems. For instance, for a 40,000-core parallel execution on Hopper, an AMD Magny-Cours based system, we achieve a BFS performance rate of 17.8 billion edge visits per second on an undirected graph of 4.3 billion vertices and 68.7 billion edges with skewed degree distribution.
Polynomial Estimators for High Frequency Moments
Venue: arXiv cs.DS 2011
Year: 2011

We present an algorithm for computing $F_p$, the $p$th moment of an $n$-dimensional frequency vector of a data stream, for $2 < p < \log (n) $, to within $1\pm ε$ factors, $ε\in [n^{-1/p},1]$ with high constant probability. Let $m$ be the number of stream records and $M$ be the largest magnitude of a stream update. The algorithm uses space in bits $$ O(p^2ε^{-2}n^{1-2/p}E(p,n) \log (n) \log (nmM)/\min(\log (n),ε^{4/p-2}))$$ where, $E(p,n) = (1-2/p)^{-1}(1-n^{-4(1-2/p})$. Here $E(p,n)$ is $ O(1)$ for $p = 2+Ω(1)$ and $ O(\log n)$ for $p = 2 + O(1/\log (n)$. This improves upon the space required by current algorithms \cite{iw:stoc05,bgks:soda06,ako:arxiv10,bo:arxiv10} by a factor of at least $Ω(ε^{-4/p} \min(\log (n), ε^{4/p-2}))$. The update time is $O(\log (n))$. We use a new technique for designing estimators for functions of the form $ψ(\expect{X})$, where, $X$ is a random variable and $ψ$ is a smooth function, based on a low-degree Taylor polynomial expansion of $ψ(\expect{X})$ around an estimate of $\expect{X}$.
New Power Estimation Methods for Highly Overloaded Synchronous CDMA Systems
Venue: arXiv cs.IT 2011
Year: 2011

In CDMA systems, the received user powers vary due to moving distance of users. Thus, the CDMA receivers consist of two stages. The first stage is the power estimator and the second one is a Multi-User Detector (MUD). Conventional methods for estimating the user powers are suitable for underor fully-loaded cases (when the number of users is less than or equal to the spreading gain). These methods fail to work for overloaded CDMA systems because of high interference among the users. Since the bandwidth is becoming more and more valuable, it is worth considering overloaded CDMA systems. In this paper, an optimum user power estimation for over-loaded CDMA systems with Gaussian inputs is proposed. We also introduce a suboptimum method with lower complexity whose performance is very close to the optimum one. We shall show that the proposed methods work for highly over-loaded systems (up to m(m + 1) =2 users for a system with only m chips). The performance of the proposed methods is demonstrated by simulations. In addition, a class of signature sets is proposed that seems to be optimum from a power estimation point of view. Additionally, an iterative estimation for binary input CDMA systems is proposed which works more accurately than the optimal Gaussian input method.
Rounding Semidefinite Programming Hierarchies via Global Correlation
Venue: arXiv cs.DS 2011
Year: 2011

We show a new way to round vector solutions of semidefinite programming (SDP) hierarchies into integral solutions, based on a connection between these hierarchies and the spectrum of the input graph. We demonstrate the utility of our method by providing a new SDP-hierarchy based algorithm for constraint satisfaction problems with 2-variable constraints (2-CSP's). More concretely, we show for every 2-CSP instance I a rounding algorithm for r rounds of the Lasserre SDP hierarchy for I that obtains an integral solution that is at most \eps worse than the relaxation's value (normalized to lie in [0,1]), as long as r > k\cdot\rank_{\geq θ}(\Ins)/\poly(\e) \;, where k is the alphabet size of I, $θ=\poly(\e/k)$, and $\rank_{\geq θ}(\Ins)$ denotes the number of eigenvalues larger than $θ$ in the normalized adjacency matrix of the constraint graph of $\Ins$. In the case that $\Ins$ is a \uniquegames instance, the threshold $θ$ is only a polynomial in $\e$, and is independent of the alphabet size. Also in this case, we can give a non-trivial bound on the number of rounds for \emph{every} instance. In particular our result yields an SDP-hierarchy based algorithm that matches the performance of the recent subexponential algorithm of Arora, Barak and Steurer (FOCS 2010) in the worst case, but runs faster on a natural family of instances, thus further restricting the set of possible hard instances for Khot's Unique Games Conjecture. Our algorithm actually requires less than the $n^{O(r)}$ constraints specified by the $r^{th}$ level of the Lasserre hierarchy, and in some cases $r$ rounds of our program can be evaluated in time $2^{O(r)}\poly(n)$.
Multiple-Source Single-Sink Maximum Flow in Directed Planar Graphs in O(diameter*n*log(n)) Time
Venue: arXiv cs.DM 2011
Year: 2011

We develop a new technique for computing maximum flow in directed planar graphs with multiple sources and a single sink that significantly deviates from previously known techniques for flow problems. This gives rise to an O(diameter*n*log(n)) algorithm for the problem.
Min-cuts and Shortest Cycles in Planar Graphs in O(n log log n) Time
Venue: arXiv cs.DS 2011
Year: 2011

We present a deterministic O(n log log n) time algorithm for finding shortest cycles and minimum cuts in planar graphs. The algorithm improves the previously known fastest algorithm by Italiano et al. in STOC'11 by a factor of log n. This speedup is obtained through the use of dense distance graphs combined with a divide-and-conquer approach.
Duplication of Key Frames of Video Streams in Wireless Networks
Venue: arXiv cs.NI 2011
Year: 2011

In this paper technological solutions for improving the quality of video transfer along wireless networks are investigated. Tools have been developed to allow packets to be duplicated with key frames data. In the paper we tested video streams with duplication of all frames, with duplication of key frames, and without duplication. The experiments showed that the best results are obtained by duplication of packages which contain key frames. The paper also provides an overview of the coefficients describing the dependence of video quality on packet loss and delay variation (network jitter).
Web services synchronization health care application
Venue: arXiv cs.DB 2011
Year: 2011

With the advance of Web Services technologies and the emergence of Web Services into the information space, tremendous opportunities for empowering users and organizations appear in various application domains including electronic commerce, travel, intelligence information gathering and analysis, health care, digital government, etc. In fact, Web services appear to be s solution for integrating distributed, autonomous and heterogeneous information sources. However, as Web services evolve in a dynamic environment which is the Internet many changes can occur and affect them. A Web service is affected when one or more of its associated information sources is affected by schema changes. Changes can alter the information sources contents but also their schemas which may render Web services partially or totally undefined. In this paper, we propose a solution for integrating information sources into Web services. Then we tackle the Web service synchronization problem by substituting the affected information sources. Our work is illustrated with a healthcare case study.
The Complexity of Surjective Homomorphism Problems -- a Survey
Venue: arXiv cs.CC 2011
Year: 2011

We survey known results about the complexity of surjective homomorphism problems, studied in the context of related problems in the literature such as list homomorphism, retraction and compaction. In comparison with these problems, surjective homomorphism problems seem to be harder to classify and we examine especially three concrete problems that have arisen from the literature, two of which remain of open complexity.
Tracking Target Signal Strengths on a Grid using Sparsity
Venue: arXiv cs.SY 2011
Year: 2011

Multi-target tracking is mainly challenged by the nonlinearity present in the measurement equation, and the difficulty in fast and accurate data association. To overcome these challenges, the present paper introduces a grid-based model in which the state captures target signal strengths on a known spatial grid (TSSG). This model leads to \emph{linear} state and measurement equations, which bypass data association and can afford state estimation via sparsity-aware Kalman filtering (KF). Leveraging the grid-induced sparsity of the novel model, two types of sparsity-cognizant TSSG-KF trackers are developed: one effects sparsity through $\ell_1$-norm regularization, and the other invokes sparsity as an extra measurement. Iterative extended KF and Gauss-Newton algorithms are developed for reduced-complexity tracking, along with accurate error covariance updates for assessing performance of the resultant sparsity-aware state estimators. Based on TSSG state estimates, more informative target position and track estimates can be obtained in a follow-up step, ensuring that track association and position estimation errors do not propagate back into TSSG state estimates. The novel TSSG trackers do not require knowing the number of targets or their signal strengths, and exhibit considerably lower complexity than the benchmark hidden Markov model filter, especially for a large number of targets. Numerical simulations demonstrate that sparsity-cognizant trackers enjoy improved root mean-square error performance at reduced complexity when compared to their sparsity-agnostic counterparts.